To highlight the superior classification performance of DRF, we have included a comparative study among DRF and the traditionally used CNN features extracted from VGGnet [1]} and MR features (Table REF ). The DRF method performs better than both the features for all three classification experiments. The lowest overall accuracy was achieved by the flat multi-class classification method (57.6%). Additionally, a very low mean f1-score of 0.05 was observed, since many classes among the total 145 had very few samples for training and testing. Nonetheless, the flat classification method achieved the highest precision (71%) for kelps among all the three methods. Out of every 100 kelp samples, this method correctly identifies 71 samples as kelps. However, this method resulted in the worst recall value of 65% (Table REF ).
